2024/12/16 by Arkaprava Saha, Bogdan Cautis, Saha, Arkaprava +5
Computer Science · Mathematics · #Data Analysis with R #Databases (cs.DB) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Social and Information Networks (cs.SI) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2412.11827
openalex publication_date 2024/12/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
We study the problem of robust influence maximization in dynamic diffusion networks. In line with recent works, we consider the scenario where the network can undergo insertion and removal of nodes and edges, in discrete time steps, and the influence weights are determined by the features of the corresponding nodes and a global hyperparameter. Given this, our goal is to find, at every time step, the seed set maximizing the worst-case influence spread across all possible values of the hyperparameter. We propose an approximate solution using multiplicative weight updates and a greedy algorithm, with provable quality guarantees. Our experiments validate the effectiveness and efficiency of the proposed methods.